Spatiotemporal Variations of Bioaerosols in the Vicinity of an Animal Feeding Operation Facility in the US

Bioaerosol emissions from animal feeding operation (AFO) facilities are of increasing interest due to the magnitude of the emissions and their potential health effect on local communities. There is limited information about fate and transport of AFO bioaerosol emissions. In this study, concentrations of airborne bacteria and fungi were measured at four ambient stations in four wind directions surrounding an egg production farm through winter, spring and summer using Andersen six-stage samplers. Mean concentrations of ambient bacteria and fungi ranged from 8.7 × 102 CFU m-3 to 1.3 × 103 CFU m-3 and from 2.8 × 102 CFU m-3 to 1.4 × 103 CFU m-3, respectively. Ambient bacterial concentrations were not significantly different over the seasons, while ambient fungal concentrations were the highest in summer and the lowest in winter. There were significant differences between downwind and upwind bacterial concentrations (p < 0.0001). Downwind bacterial and fungal concentrations responded differently to the influencing factors. Bacterial concentrations were quadratically correlated with wind vector (combined effects of wind speed and direction) and emission rate, were positively correlated with temperature, and were negatively correlated with solar radiation. Fungal concentrations were positively correlated with temperature, RH, and emission rate, and were negatively correlated with wind vector.

Elemental Compositions and Chemical Mass Closure of Fine Particulate in an Animal Feeding Operation Facility and Its Vicinity

Particulate matter (PM) emissions from animal feeding operations (AFOs) have been considered as an important contributor to ambient PM in rural areas. Investigation of the chemical compositions of PM2.5 inside and in the vicinity of AFOs can enhance our understanding of the AFO emissions impact on ambient PM characteristics. This year-long field study was conducted on a commercial egg production farm to investigate ambient PM chemical compositions as impacted by the air emissions from the production houses. The PM2.5 samples were collected from five sampling stations (one in-house station and four ambient locations in four wind directions). The trace elements, major ions, organic carbon (OC) and element carbon (EC) were analyzed by X-ray florescence (XRF), ion chromatography (IC), and thermo-optical analyzer, respectively. There were significant differences in elemental compositions between PM samples from in-house station (ST1) and ambient stations (ST2-ST5). The chemical mass balance analysis revealed that OC accounted for above 50% of PM2.5 mass at in-house and ambient stations;NH4+, SO42-, and NO3- accounted for about 40.0% of the total PM2.5 mass in ambient locations and for only 12% of the total PM2.5 mass in house. The measured PM2.5 masses agreed with the sums of the masses of chemical compositions at all stations except for the in-house station. Knowledge gained from this study, with additional consideration of NH3 concentrations and emissions, will lead to better understanding of PM2.5 source and formation, fate and transport, and their atmospheric dynamics.

Operations Improvement Function and Organisational Adaptability of Petroleum Tank Farms in South-South, Nigeria

The study examined the nexus between operations improvement function (dimensioned by contingency planning, benchmarking and continuous improvement processes) and organisational adaptability of Petroleum tank farms in South-South, Nigeria. The contingency theory and the theory of routine dynamics underpinned the study, and positivism was the underlying philosophy. The study adopted the cross-sectional survey through the use of questionnaire. 820 middle and top-level managers constituted the elements of the population, and the Krejcie & Morgan’s formula was used to determine the sample size of 262 respondents. Structural Equation Modeling was deployed to test the hypotheses at a 0.05 significance level. The results showed that contingency planning;benchmarking and continuous improvement processes all have a significant positive relationship with organisational adaptability of Petroleum tank farms in South-South, Nigeria. The study concludes that Petroleum tank farms’ operations should focus on the adoption of contingency planning, benchmarking and continuous improvement processes to enhance organisational adaptability. Therefore, it is recommended that the management of Petroleum tank farms should put in place mechanisms to advance continuous improvement processes by allocating the necessary amount of resources, such as energy, time and money, in order to promote the continuous development of the continuous improvement systems. Furthermore, managers of Petroleum tank farms should make better the adoption of contingency planning, ensuring that there is as much necessary training and information for employees on how to act during a crises situation, in order to evaluate safety and prepare in advance for recovery from disasters.

Review of application of artificial intelligence techniques in petroleum operations

In the last few years,the use of artificial intelligence(AI)and machine learning(ML)techniques have received considerable notice as trending technologies in the petroleum industry.The utilization of new tools and modern technologies creates huge volumes of structured and un-structured data.Organizing and processing of these information at faster pace for the performance assessment and forecasting for field development and management is continuously growing as an important field of investigation.Various difficulties which were faced in predicting the operative features by utilizing the conventional methods have directed the academia and industry toward investigations focusing on the applications of ML and data driven approaches in exploration and production operations to achieve more accurate predictions which improves decision-making processes.This research provides a review to examine the use cases and application of AI and ML techniques in petroleum industry for optimization of the upstream processes such as reservoir studies,drilling and production engineering.The challenges related to routine approaches for prognosis of operative parameters have been evaluated and the use cases of performance optimizations through employing data-driven approaches resulted in enhancement of decision-making workflows have been presented.Moreover,possible scenarios of the way that artificial intelligence will develop and influence the oil and gas industry and how it may change it in the future was discussed.

Heavy-organic particle deposition from petroleum fluid flow in oil wells and pipelines

Suspended asphaltenic heavy organic particles in petroleum fluids may stick to the inner walls of oil wells and pipelines. This is the major reason for fouling and arterial blockage in the petroleum industry. This report is devoted the study of the mechanism of migration of suspended heavy organic particles towards the walls in oil-producing wells and pipelines. In this report we present a detailed analytical model for the heavy organics suspended particle deposition coefficient corresponding to petroleum fluids flow production conditions in oil wells. We predict the rate of particle deposition during various turbulent flow regimes. The turbulent boundary layer theory and the concepts of mass transfer are utilized to model and calculate the particle deposition rates on the walls of flowing conduits. The developed model accounts for the eddy diffusivity, and Brownian diffusivity as well as for inertial effects. The analysis presented in this paper shows that rates of particle deposition （during petroleum fluid production） on the walls of the flowing channel due solely to diffusion effects are small. It is also shown that deposition rates decrease with increasing particle size. However, when the process is momentum controlled （large particle sizes） higher deposition rates are expected.

Modeling on a Modified Feeding Mode of Simulated Moving Bed for Improving Productivity

A new feeding mode for a simulated moving bed(SMB) is proposed.The outlet stream from zone II is collected at regular intervals.The concentration of the solution is increased by dissolving raw materials and then fed to zone III as the feed stream during the next collection interval.In this feeding mode,the concentration of the stream fed to zone III is identical to that of original feed,while in a conventional SMB,the feed is diluted by mix-ing with the outlet stream of zone II before feeding to zone III.The new feeding mode increases the inlet concentra-tion of zone III.A modeling investigation shows that higher inlet concentration of zone III increases the height of concentration band in SMB,improving the separation performance significantly.In comparison with the traditonal feeding mode,the new feeding mode increases the productivity by 23.52%and decreases the solvent consumption by 22.56%,so as to increase the raffinate and extract concentrations by 53.17%and 20.38%,respectively.The col-lection interval for the outlet stream from zone II has no effect on the separation performance after reaching the steady state,so that the collection interval can be increased to make the operation more convenient.

The Influence of Feeding Method on Fluidization Behavior of Fixed Fluidized Bed

An experimental installation of cold model simulation was set up to study the bed pressure drop in different regions of fixed fluidized bed reactor during top feeding and bottom feeding, respectively, at various gas velocities with the fluidization image of solid particles monitored at the same time. By comparing the changes in bed density and operating gas velocity in different regions of fixed fluidized bed reactor, the influence of top feeding and bottom feeding patterns on fluidization behavior could be investigated. The results showed that the bed density in top feeding reactor responded more stably to the change in gas velocity along with the advantage of working in a wider range of operating gas velocities. Based on this study, it is concluded that existing bottom feeding reactor configurations cannot meet the fluidization requirements; and optimization of bottom feeding reactor will be needed.

液化气脱硫醇装置尾气治理改造及运行总结

针对中国石化济南分公司液化气脱硫醇装置尾气治理部分工艺流程存在的一些问题,对尾气治理部分工艺流程进行了升级改造,实现了脱硫醇尾气“零排放”,同时对运行期间主要设备出现的异常情况进行了原因分析,并进行相应的处置措施,确保装置平稳安全运行。

高质量发展视域下高校饲料专业学生社会服务能力提升策略研究

社会服务是高校的重要职能,高质量发展对高校学生社会服务能力的提升提出新要求。为此,本文从高校饲料专业角度出发,深入分析学生社会服务能力提升的逻辑起点,并针对其中的现实挑战提出了高校饲料专业学生社会服务能力提升的实践路径。以期培养高质量的饲料专业人才,为经济与社会高质量发展提供有力的人才保障与智力支撑。

催化裂化装置液化石油气进料泵振动超标工艺处置和优化改造

中国石油四川石化有限责任公司250万t/a催化裂化装置产品精制单元的液化石油气(简称液化气)进料泵频繁出现振动超标后干气密封泄漏故障,导致装置生产波动或非计划停工。分析了液化气进料泵故障产生原因。针对液化气进料泵振动超标后阀门内漏导致的无法切换隔离问题,创新使用了扩大流程法,实现了液化气进料泵的安全有效切换隔离。采用调整额定体积流量、更换泵型号、更换电机和机械密封等方法对液化气进料泵进行了技术改造优化,使液化气进料泵振动值从7~10 mm/s大幅降至2.1 mm/s以下,对催化裂化装置长周期安全平稳运行起到了积极作用。

基于相似性原理的燃气电厂给水泵异常检测方法研究

设备运行监盘是燃气电厂重要的工作内容,提前识别设备运行中可能出现的异常并进行可靠预警,对保障电厂安全平稳运行至关重要。文章以燃气电厂给水泵为研究对象,基于大量运行历史数据,开发了一种基于相似性原理(SBM)和序列概率比检验(SPRT)的异常检测算法模型。通过建立当前观测向量与代表设备健康运行状态矩阵之间的相似度模型,得到各特征当前时刻预估值,并对不同时刻下各特征的模型预测残差进行均值检验,识别设备运行异常。结果表明,该预警方法可以使参数在偏离正常运行工况还未超过控制系统报警值之前,发出预警,弥补了原有工控系统固定值报警以及人工监盘导致的感知滞后、监视不到位的问题,为设备预测性维护提供技术支撑。

无人驾驶虾蟹投饲自主作业船及智能管控平台研发与应用

本文结合多功能无人驾驶投饲船,基于“物联网+大数据”技术,开发一种多水域集群作业自主导航虾蟹养殖船智能管控平台。可在手机或电脑端对多片水域的多条无人船进行远程集群调度,按时按需精准投饵。通过无人船上的水质传感器实时监测水体的各类水质参数,对异常水域在后台直观显示、预警,并调用对应水域的无人船开展有针对性的施药喷洒防治。大幅降低人工成本,提高水产养殖效益。可为拥有较大水域多片水域的大规模虾蟹水产养殖户或众多委托管理的单水域养殖户提供作业和管理服务,使水产养殖装备向着自动化、信息化、规模化、精确化方向发展,大幅降低人工成本,提高水产养殖效益,对虾蟹养殖技术的发展具有十分重要的意义。

工厂化养蜂自动饲喂系统设计

在冬季无花期,蜜蜂无法到户外采蜜,需要人工饲喂。但人工饲喂工作效率低、劳动强度大,需要往返养蜂场。设计了一套自动饲喂蜜蜂的控制系统,提出了4种工作模式,使养蜂人员在HMI、手机端、PC端均可控制饲喂设备,实现手动饲喂、自动定量定时饲喂,以及管路和泵清洗,降低了劳动成本,而且干净卫生。系统还具备数据自动存储和智能报警功能,经测试,系统运行平稳。

电磁振动机的研究与应用

电磁振动机结构简单、通用性好,常用作振动喂料器并广泛应用于现代工业的多个领域。该文概述了电磁振动机的结构组成、工作原理、分类及其应用范围,针对电磁振动机定量给料、快速光电检测及定向种植领域内的最新研究进展进行了总结,以期为我国电磁振动机未来发展趋势与研究方向提供参考借鉴。

“双创”背景下饲料创业项目商业化运作研究

在大众创业、万众创新背景下,创业项目的增多能有效激发经济发展活力,以创业带动就业,缓解社会就业矛盾。饲料产业不仅是传统加工业的重要组成部分,还是种植业和畜牧养殖业之间的中轴产业,在农业经济发展中发挥着重要的支撑作用。面对饲料产业转型升级和产业链整合的重大机遇,越来越多的饲料创业项目进入商业化运作阶段。为此,文章从饲料创业项目的可行性分析入手,对饲料创业项目商业化运作中面临的问题提出改善建议,以期有效提高饲料创业项目的成功率,使其能尽快投产,推动饲料产业的高质量发展。

石油钻井带压作业技术工艺

带压作业是保持井筒内一定压力,不压井、不放井的情况下进行管柱起下的先进井下作业技术,广泛应用于石油钻井中,如油田欠平衡钻井、修完井等工作,应用堵塞器等工具将管柱内部封堵,利用带压作业机的动密封型防喷器控制环空压力,通过举升系统和旋转系统上举、下压和旋转管柱,以实现带压作业。因此,本文主要进行带压作业技术概述,探究石油钻井带压作业技术工艺的应用。

基于供电能力的行车动态卡控方法

[目的]在城市轨道交通线路中,牵引供电设施支持列车运行的数量具有上限。当前续列车故障或者繁忙线路列车追踪密度过高时,可能导致供电臂负荷电流过大而引起电力开关跳闸,从而严重影响运营。传统的ATS(列车自动监控)系统无法智能化掌握供电系统的实时运行情况,需研究供电能力和行车的关系以实现行车动态卡控。[方法]基于供电臂供电能力分析以及列车运行曲线,提出一种行车动态卡控方法。通过对信号、供电、车辆和轨道等多专业数据进行融合分析,并结合大数据电流曲线预测模型,实现对供电负载能力的动态监测与预测。将预测的电流曲线与列车ATO(列车自动运行)运行曲线有机结合,实时调整进入当前供电臂运行的列车数,实现动态卡控列车运行。该方法在深圳地铁16号线上进行了实例验证。[结果及结论]基于供电能力的行车卡控方法通过预测列车所需取流动态调整列车运行,可有效避免因行车导致过负荷引起的供电系统跳闸故障,在保证运营效率的同时,提高了线路运营的安全性。

基于优化传递函数的机床微进给控制方法仿真

高强度运行环境下,数控机床往往要求较高的加工精度,为了提升机床微进给控制性能,提出一种基于优化传递函数的机床微进给控制方法。对机床微进给架构的驱动部分建模分析,获取驱动部分的传递函数与传动部分的传递函数,根据传递函数分析机床稳定性。将机床微进给全局滑模控制和内模控制有效结合,在高强度运行环境下建立一种全新的机床微进给控制器,通过新控制器实现高强度运行环境下机床微进给控制。仿真测试结果表明,所提方法可以有效降低机床运行能耗,提高工件加工效率,降低控制误差,说明上述方法可以取得预期控制效果。

锅炉开车平稳投返灰操作探讨

锅炉装置自投产以来,开车投返灰加负荷过程中出现床温频繁波动,轻则导致蒸汽系统不稳定,重则导致锅炉熄火停车或结焦事故。通过生产实践总结,提出锅炉开车平稳投返灰操作办法,确保装置安全、稳定运行。